![]() The community indices are influenced by the course of time when separate periods of time are compared. The effect of the passage of time on oribatid communities was also analyzed by comparing recent communities to those of 19–26 years ago in the same forests. Differences in community indices were detected only for species abundances, with holm oak showing the highest oribatid density and beech the lowest. By contrast, the influence of spatial distribution (geography) was not significant by itself but played an important role as a co-variable. The local scale variable (forest type, 28 %) was about as determinant a factor as the regional scale (climatic region, 26 %), though together they accounted for just 9 %. Forest type and climatic region together (45 % of the total variability) were important factors influencing the oribatid community. The suffix for the wide variables, e.g.Oribatid mite communities from 18 natural autochthonous forest soils in the Basque Country, belonging to five forest types, distributed along an ombrothermic gradient of five climatic regions were broadly studied. Var-with-suffix is the variable from the long file that contains Wide-id-var is the variable that uniquely identifies wide Long-var(s) is the name of the long variable(s) to be made wide e.g. reshape wide long-var(s), i( wide-id-var ) j( var-with-suffix ) where The general syntax of reshape wide can be expressedĪs. Reshape wide age, j(birth) i(famid) Wide format J variable (2 values) dadmom -> (dropped) reshape wide name inc, i(famid) j(dadmom) string The string option to tell Stata that the suffix is character. Let’s reshape this to be in a wide format, containing one record per family. Reshape can handle character suffixes as well.Ĭonsider the dadmoml data file shown below. The examples above showed how to reshape data using numeric suffixes, but Reshape wide kidname age wt sex, i(famid) j(birth)Įxample #3: Reshaping wide with character suffixes Reshape the variables age, wt and sex like this. In the example above, we just reshaped the age variable. The reshape command can work on more than one variable at a time. J(birth) tells reshape that the suffix of age (1 2 3) should be taken from the variable birth Example #2: Reshaping data long to wide with more than one variable I(famid) tells reshape that famid uniquely identifies observations in the wide form Stata that the variable to be converted from long to wide is age Wide tells reshape that we want to go from long to wide Let’s look at the pieces of the reshape command. ( age2 would be missing if there is only 1 kid, and age3 would be missing if there are only 2 ![]() Let’s make age in this file wide, making one record per family which would contain age1 age2 age3, the ages of the kids in the family (To make things simple at first, we will drop the variables kidname sex and The reshape command can be used to make data from a long format to a wide format. ![]() Show common examples of reshaping data but do not exhaustively demonstrate theĭifferent kinds of data reshaping that you could encounter. These examples take long data files and reshape them into wide form. And simplicity of Stata in its ability to reshape data files.
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